update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- wikiann
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: bert-large-uncased_ner_wikiann
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wikiann
+      type: wikiann
+      args: en
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8383588049015558
+    - name: Recall
+      type: recall
+      value: 0.8608794005372543
+    - name: F1
+      type: f1
+      value: 0.8494698660714285
+    - name: Accuracy
+      type: accuracy
+      value: 0.9379407966623622
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# bert-large-uncased_ner_wikiann
+
+This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the wikiann dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3373
+- Precision: 0.8384
+- Recall: 0.8609
+- F1: 0.8495
+- Accuracy: 0.9379
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.3146        | 1.0   | 1250 | 0.2545          | 0.7956    | 0.8372 | 0.8159 | 0.9285   |
+| 0.1973        | 2.0   | 2500 | 0.2438          | 0.8267    | 0.8546 | 0.8404 | 0.9349   |
+| 0.1181        | 3.0   | 3750 | 0.2637          | 0.8320    | 0.8588 | 0.8452 | 0.9374   |
+| 0.0647        | 4.0   | 5000 | 0.3175          | 0.8389    | 0.8627 | 0.8507 | 0.9387   |
+| 0.0443        | 5.0   | 6250 | 0.3373          | 0.8384    | 0.8609 | 0.8495 | 0.9379   |
+
+
+### Framework versions
+
+- Transformers 4.20.1
+- Pytorch 1.11.0
+- Datasets 2.1.0
+- Tokenizers 0.12.1